Category: Generative AI

Navigating GPU resource constraints requires a multi-faceted approach spanning procurement strategies, leveraging AWS AI accelerators, exploring alternative compute options, utilizing managed services like SageMaker, and implementing best practices for GPU sharing, containerization, monitoring, and cost governance. By adopting these techniques holistically, organizations can efficiently and cost-effectively execute AI, ML, and GenAI workloads on AWS, even amidst GPU scarcity. Importantly, these optimization strategies will remain valuable long after GPU supply chains recover, as they establish foundational practices for sustainable AI infrastructure that maximizes performance while controlling costs—an enduring priority for organizations scaling their AI initiatives into the future.

Amazon EC2 and SageMaker AI are two of the foundational AWS services for Generative AI. Amazon EC2 provides the scalable computing power needed for training and inference, while SageMaker AI offers built-in tools for model development, deployment, and optimization. Cost optimization is crucial since Generative AI workloads require high-performance accelerators (GPU, Trainium, or Inferentia) and extensive processing, which can become expensive without efficient resource management. By leveraging the below cost optimization strategies, you can reduce costs while maintaining performance and scalability.